Discovering and understanding the mechanisms underlying complex phenotypical
traits is of primary importance in bio-medical research. A deeper and detailed
knowledge of the physio-pathological events leading to the onset and
progression of a disease enables a clearer estimation of disease risk and more
accurate diagnosis, prognosis evaluation, and decision making, including
treatment choice [1]}. With the advent of the
high-throughput sequencing (HTS) technologies, the actual complexity behind
diseased (and generally, phenotypical) traits became prominent, opening
up to the big data era also in molecular biology and medicine
[2]}. Biological systems complexity arises from the
interactions and reactions among their components (e.g., genomic elements,
epigenomic modifications, DNA-binding proteins, miRNAs, receptors, signaling
molecules) and the layered modularity of their compartments (e.g., cellular
components, tissues, organs). Predicting the behavior of these components
after external perturbation or intrinsic variability (e.g., genetic
polymorphisms), is key for the discovery and prediction of disease-associated
processes [3]}, [1]}, [2]}.
Biological models are commonly represented by signaling pathways, chains of
metabolic reactions, disease modules, or very large protein-protein interaction
networks (also called interactomes) [1]}, [7]}.
Given the vast amount of publicly available bio-medical databases, the
access to curated biological models is no longer a limitation. The key
feature of these databases is the availability of structured bio-chemical
and bio-medical information that can be readily converted into networks
and statistical models: we generally refer to them as knowledge-based
models (KBMs). KBMs provide a basis and a gold standard to improve
exploratory methods, with some critical issues [1]}.
Fistly, defining a set of rules to convert a KBM to a causal model is key
to test specific biological hypotheses and mechanisms, but it is not always
trivial due to missing information
or evidence level (e.g., experimental evidence versus inference from
similarity or electronic annotation).
Secondly, a KBM reflects current knowledge, constantly challenged by new
experimental data that may reveal novel interactions and pathways.
Finally, there must be clear statistical criteria to evaluate the initial
causal model, reflecting biological properties of the system, and
improving both model descriptive and predictive power
[3]}, [1]}.
Starting from current knowledge, network models should be updated and
tested in a simple and clear workflow. From the computational point of
view, the challenge is to free the user from chosing the initial setup,
estimating algorithm and model parameters directly from quantitative
data, with eﬃcient and parallelizable methods.
